class_docstr: A module to perform token level classification tasks such as Named entity recognition.
class_docstr_tok: ["A","module","to","perform","token","level","classification","tasks","such","as","Named","entity","recognition","."]
docstr: Performs the forward step of the module.
  Args:
      hidden_states: batch of hidden states (for example, from the BERT encoder module)
          [BATCH_SIZE x SEQ_LENGTH x HIDDEN_SIZE]
  Returns: logits value for each class [BATCH_SIZE x SEQ_LENGTH x NUM_CLASSES]
docstr_tok: ["Performs","the","forward","step","of","the","module",".","Args",":","hidden_states",":","batch","of","hidden","states","(","for","example",",","from","the","BERT","encoder","module",")","[","BATCH_SIZE","x","SEQ_LENGTH","x","HIDDEN_SIZE","]","Returns",":","logits","value","for","each","class","[","BATCH_SIZE","x","SEQ_LENGTH","x","NUM_CLASSES","]"]
code: def forward(self, hidden_states):
      """
      Performs the forward step of the module.
      Args:
          hidden_states: batch of hidden states (for example, from the BERT encoder module)
              [BATCH_SIZE x SEQ_LENGTH x HIDDEN_SIZE]
      Returns: logits value for each class [BATCH_SIZE x SEQ_LENGTH x NUM_CLASSES]
      """
      hidden_states = self.dropout(hidden_states)
      logits = self.mlp(hidden_states)
      return logits
  
file_path: NeMo/nemo/collections/nlp/modules/common/token_classifier.py
imports: from dataclasses import dataclass
  from typing import Dict, Optional
  from torch import nn
  from nemo.collections.common.parts import MultiLayerPerceptron
  from nemo.collections.nlp.modules.common.classifier import Classifier
  from nemo.core.classes import typecheck
  from nemo.core.neural_types import LogitsType, LogprobsType, NeuralType
label_desc: Use image node_id 3 for calling the TokenClassifier obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: logits